Introduction

Here we will investigating changing climatic trends from 1999-2000 in Cape Florida, FL.

cape_map %>% 
  leaflet::addCircleMarkers(data = cape, 
                                               lng = ~long, 
                                               lat = ~lat, 
                                               label = c("Cape Florida"), 
                                               color = "orange", 
                                               labelOptions = labelOptions(noHide = T, 
                                               textsize = "15px")) %>% 
  addTiles() %>% 
  addEsriBasemapLayer(esriBasemapLayers$Imagery)
florida <- read_csv("florida.csv") %>% 
  clean_names() %>% 
  mutate(date = as_date(date), 
         year = year(date), 
         month = month(date), 
         day = day(date)) %>% 
  select(date, year, month, day, prcp, tmax, tmin)

Trend Analysis Results

Assess whether climate at your location has been changing with trend analysis - using 2 different metrics for averages - using 2 different metrics for extremes

a+ 
  stat_smooth(data=fl_annual,
               aes(x=year,mean_min),
               col="light seagreen", method="lm")+
  theme_minimal()+
  labs(x = "Year", 
       y = "Average Annual Temperature (ºF)", 
       title = "Average daily minimmum and maximum average temperatures from 1999-2020", 
       caption = "Here the average daily minimum and maximum temperatures are shown over time. \nThe red dots represent the average maximum daily temperature, and the red line is a linear trendline\n of the changes over time. The blue dots represent the average minimum daily temperature, and the blue line is a\n linear trensline representing changes over time.")

Discussion

Why are these metrics likely to be relevant, given the climate change impacts you expect at this location?

Use some good old T, rank-sum, or other statistical tests,

Citations